Federated Learning for Physical Layer Design

Model-free techniques, such as machine learning
(ML), have recently attracted much interest towards the physical
layer design, e.g., symbol detection, channel estimation, and
beamforming. Most of these ML techniques employ centralized
learning (CL) schemes and assume the availability of datasets at a
parameter server (PS), demanding the transmission of data from
edge devices, such as mobile phones, to the PS. Exploiting the data
generated at the edge, federated learning (FL) has been proposed
recently as a distributed learning scheme, in which each device
computes the model parameters and sends them to the PS for
model aggregation while the datasets are kept intact at the edge.
Thus, FL is more communication-efficient and privacy-preserving
than CL and applicable to the wireless communication scenarios,
wherein the data are generated at the edge devices. This article
presents the recent advances in FL-based training for physical
layer design problems. Compared to CL, the effectiveness of FL
is presented in terms of communication overhead with a slight
performance loss in the learning accuracy. The design challenges,
such as model, data, and hardware complexity, are also discussed
in detail along with possible solutions.